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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

共 1678 条记录，以下是361-370 订阅

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Content-based Image Retrieval based on Convolutional Neural Networks 10

Content-based Image Retrieval based on Convolutional Neural ...

引用

10th IEEE international conference on Intelligent computing and Information Systems, ICICIS 2021

作者： Ghaleb, Moshira S. Ebied, Hala M. Shedeed, Howida A. Tolba, Mohamed F. Ain Shams University Scientific Computing Faculty of Computer and Inofrmation Sciences Cairo Egypt

ISBN: (纸本)9781665440769

Images can communicate a service, brand or product. Moreover, images provide depth and context to a description or story and give a much more intense experience than writing alone. Image retrieval is the highest searching performance process, especially in large databases. Search by content is a powerful tool for many industries. Images are more complicated than texts. Each image has important features like color, texture, and edges. Searching by part of image is termed content-based image retrieval. Convolution neural network achieved high accuracies in images feature extraction and classification. For high-performance image retrieval, we proposed an intelligent model using CNN. the model was applied on Cifar10 and Mnist datasets. the proposed model achieved 92.9% and 99.8% accuracies for Cifar10 and Mnist datasets respectively. the model achieved the highest accuracies and best processing performance compared by State-of-arts-models. © 2021 IEEE.

关键词： Convolutional neural networks

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GPGPU based Concurrent Classification using Trained Model of Handwritten Digits

GPGPU based Concurrent Classification using Trained Model of...

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10th international conference on Open Source Systems and Technologies (ICOSST)

作者： Rizvi, Syed Tahir Hussain Cabodi, Gianpiero Arif, Arslan Javed, Muhammad Yaqoob Gulzar, Muhammad Majid Politecn Torino Turin Italy Univ Sci & Technol China Hefei Peoples R China Univ Cent Punjab Lahore Pakistan

ISBN: (纸本)9781509055869

In this paper, General Purpose Graphical Processing Unit (GPGPU) based concurrent implementation of handwritten digit classifier is presented. Different styles of handwriting make it difficult to recognize a pattern but using neural network, it is not a difficult task to perform. Different softwares like torch and MATLAB provide the support of multiple training algorithms to train a network. By choosing an appropriate training algorithm for a specific application, speed of training can be increased. Furthermore, using computational power of GPUs, training and classification speed of neural network can be significantly improved. In this work, Modified National Institute of Standards and Technology (MNIST) database of handwritten digits is used to train the network. Accuracy and training time of digit classifier is evaluated for different algorithms and then concurrent training is performed by exploiting power of GPU. Trained parameters are imported and used for the concurrent classification with Compute Unified Device Architecture (CUDA) computing language which can be useful in numerous practical applications. Finally, the results of sequential and concurrent operations of training and classification are compared.

关键词： Concurrent Parallel architectures Neural network pattern recognition MATLAB Image Classification

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Stacking class probabilities obtained from view-based cluster ensembles

引用

10th international conference on Artificial Intelligence and Soft computing, ICAISC 2010

作者： Kaya, Heysem Kurşun, Olcay Şeker, Hüseyin Department of Computer Engineering Bogazici University 34342 Bebek Istanbul Turkey Department of Computer Engineering Istanbul University 34320 Avcilar Istanbul Turkey Department of Informatics Faculty of Technology De Montfort University Leicester LE19BH United Kingdom

ISBN: (纸本)3642132073

In pattern recognition applications with high number of input features and insufficient number of samples, the curse of dimensionality can be overcome by extracting features from smaller feature subsets. the domain knowledge, for example, can be used to group some of the features together, which are also known as "views". the features extracted from views can later be combined (i.e. stacking) to train a final classifier. In this work, we demonstrate that even very simple features such as class-distributions within clusters of each view can serve as such valuable features. © 2010 Springer-Verlag.

关键词： pattern recognition

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Research on modeling for the pattern library of interconnect parasitic capacitances in VLSI

Research on modeling for the pattern library of interconnect...

引用

2010 10th IEEE international conference on Solid-State and Integrated Circuit Technology

作者： Qu, Hui Xu, Xiaoyu Ren, Zhuoxiang Institute of Electrical Engineering Chinese Academy of Sciences Beijing 100190 China L2E UPMC University of Paris 6 4 place Jussieu F-75252 Paris France

ISBN: (纸本)9781424457984

At the present IC technologies, the accurately extraction of the interconnects parasitic parameters become more important. But for the time consuming, that computing the parameters of interconnects with field solver directly is impracticable. the common way is that establishing the pattern library according some typical Structures at the early design stage, then calculating the actual parameters after routing using these patterns. But for the pattern library establishment of interconnects parasitic parameters, how to determine the form of the pattern and how much sampling points should be selected, that are troublesome, which is related to both the time consuming and the accuracy of the model. In this paper, the authors provide a new modeling method, called error modification model method, and adopt a orthogonal sampling method, it can construct a parasitic parameter model in a relatively short time with high precision. ©2010 IEEE.

关键词： Capacitance

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fMRI Brain Networks as Statistical Mechanical Ensembles 25

fMRI Brain Networks as Statistical Mechanical Ensembles

引用

25th international conference on pattern recognition (ICPR)

作者： Wu, Hui Wang, Jianjia Hancock, Edwin R. Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai Inst Adv Commun & Data Sci Shanghai Peoples R China Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

ISBN: (纸本)9781728188089

In this paper, we apply ensemble methods from statistical physics to analyse fMRI brain networks for Alzheimer's patients. By mapping the nodes in a network to virtual particles in a thermal system, the microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. these representations are obtained by selecting a threshold on the BOLD time series correlations between nodes in different ways. the microcanonical ensemble corresponds to a set of networks with a fixed fraction of edges, while the canonical ensemble corresponds to the set networks with edges obtained with a fixed value of the threshold. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. Our treatment allows us to specify new partition functions for fMRI brain networks, and to explore a phase transition in the degree distribution. the resulting method turns out to be an effective tool to identify the most salient anatomical brain regions in Alzheimer's disease and provides a tool to distinguish groups of patients in different stages of the disease.

关键词： Statistical Physics

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Core-set based product quantization for large-scale multimedia search 18

Core-set based product quantization for large-scale multimed...

引用

10th international conference on Internet Multimedia computing and Service, ICIMCS 2018

作者： Zhao, Tianli Jiang, Yanshu Wu, Jiaxiang Cheng, Jian Harbin University of Technology National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science China Tencent AI Lab China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science China

ISBN: (纸本)9781450365208

Approximate nearest neighbor (ANN) search has been wellstudied and weightily applied in the field of large scale multimedia search. In recent years, Product Quantization (PQ) based methods have achieved greate success in ANN search. However, both the time and space complexity for PQ learning are linear to the data amount, making it infeasible for large-scale datasets. In this paper, we propose a more efficient learning strategy for product quantization. Instead of learning PQ on the whole dataset, we construct a small set of representative elements, from which PQ can be approximated learned with much lower computation overhead. Extensive experiments are conducted on three large-scale datasets. the codebook learning process of our approach can achieve up to tens times speed-up than existing PQ-based methods, while the memory consumption is only logarithmic to the number of data points. At the same time, the ANN search accuracy of our method is still comparable with state-of-the-art methods on all datasets. © 2018 ACM.

关键词： Nearest neighbor search

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10th international Workshop on Security Proofs for Embedded Systems, PROOFS 2021

10th International Workshop on Security Proofs for Embedded ...

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10th international Workshop on Security Proofs for Embedded Systems, PROOFS 2021

the proceedings contain 6 papers. the special focus in this conference is on Security Proofs for Embedded Systems. the topics include: Locality Based Cache Side-channel Attack Detection∗;XMSS-based Chain of Trust;towa...

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the Right Delay Detecting Specific Spike patterns with STDP and Axonal Conduction Delays

The Right Delay Detecting Specific Spike Patterns with STDP ...

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10th international conference on Adaptive and Natural computing Algorithms

作者： Datadien, Arvind Haselager, Pim Sprinkhuizen-Kuyper, Ida Radboud Univ Nijmegen Dept Artificial Intelligence Nijmegen Netherlands

ISBN: (纸本)9783642202810

Axonal conduction delays should not be ignored in simulations of spiking neural networks. Here it is shown that by using axonal conduction delays, neurons can display sensitivity to a specific spatiotemporal spike pattern. By using delays that complement the firing times in a pattern, spikes can arrive simultaneously at an output neuron, giving it a high chance of firing in response to that pattern. An unsupervised learning mechanism called spike-timing-dependent plasticity then increases the weights for connections used in the pattern, and decreases the others. this allows for an attunement of output neurons to specific activity patterns, based on temporal aspects of axonal conductivity.

关键词： Spiking neural networks STDP Axonal delay Spatiotemporal pattern

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Research on Fast Adaptive Transmission Models for international Inland Port Based on Edge Intelligence 10

Research on Fast Adaptive Transmission Models for Internatio...

引用

10th IEEE international conference on Cyber Security and Cloud computing and 9th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yiwen, Liu Zhirong, Zhu Tangyan Wenkan, Wen Xiaoning, Peng Yuanquan, Shi Huaihua University School of Computer and Artificial Intelligence Huaihua Hunan418000 China Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Huaihua Hunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9798350312461

In this paper, we propose a logistics international Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. the proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua international Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations. © 2023 IEEE.

关键词： Resource allocation

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FTAB: Fault Tolerance Approach by Using HMM with BAUM-WELCH Algorithm in MCC

FTAB: Fault Tolerance Approach by Using HMM with BAUM-WELCH ...

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10th IEEE and IFIP international conference on Wireless and Optical Communications Networks (WOCN)

作者： Patel, Pushpraj Prakash, Vijay SVITS Dept Comp Sci & Engn Indore Madhya Pradesh India

ISBN: (纸本)9781467359979;9781467359993

Fault tolerance in mobile cloud computing is highly important aspect, even more than a conventional cloud computing because of the mobile nature of devices i.e. mobility. Mobile cloud computing (MCC) combines cloud computing and mobile devices to provide the benefits for mobile users, network operators and cloud providers also. Mobile devices have various problems like enter and leave the connection unpredictably, Limitation of battery power, frequent location changes, network signal loss, hardware failures and other common factors. Mobile devices appear and disappear in the network unpredictably. the proposed technique reduces the faults of mobile devices by using the past pattern of states and making the decision for predicting the future states of mobile devices. In this paper we have proposed a fault monitoring technique which is based on Hidden Markov Model (HMM) and Baum-Welch algorithm for analyzing and predicting the future resource state.

关键词： cloud computing mobile cloud computing monitoring time interval pattern and Hidden Markov model

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